code: def test_correct_values_classification():
      ground_truth, retrieved = return_ground_incorrect_retrievals()
      expected_return = {
          "precision": np.array([0.5, 1.0]),
          "recall": np.array([1.0, 0.5]),
          "f1_score": np.array([0.66666667, 0.66666667]),
          "support": np.array([2, 4]),
      }
      score = evaluate(ground_truth, retrieved, metric="classification")
      assert isinstance(score, dict)
      assert set(score.keys()) == set(
          ["precision", "recall", "f1_score", "support"]
      )
      for k, v in score.items():
          assert isinstance(v, np.ndarray)
          np.testing.assert_almost_equal(score[k], expected_return[k])
  
file_path: imagededup/tests/test_evaluator.py
imports: import pytest
  import numpy
  from imagededup.evaluation.evaluation import evaluate, _check_map_correctness, _transpose_checker
label_desc: Use image node_id 14 for calling a global function with example usage: test_correct_values_classification() without return types